Avaliação genética de bovinos Nelore para pesos até a desmama sob modelos com diferentes estruturas de grupos de contemporâneos - PÁSCOA, Lillian (2011)
Data from 72,731 Nelore calves were used to estimate (co)variances and predict breeding values for actual and adjusted weight for 120 and 210 days of age using different contemporary group structures. Males and females calves born from 1985 to 2005 belonging to 40 herds participating in the Nelore Brasil Program were analysed. Ten models were used including five different contemporary group (GC) structures, judged by coefficient of determination, residual variance and by the Akaike (AIC), Bayesian (BIC) and modified Akaike (CAIC) information criteria. The GLM procedure of SAS was used to carry out the analyses. All effects in the model were significant (P<0.001) for the traits analyzed. The inclusion of semester or trimester of birth in the composition of GC was more appropriate than when it was estimated independently as this took into account interactions with the other effects in the GC. Sex of calf (SB) and age of cow at calving (CIVP) had similar effects across models suggesting independence from other effects in these models. In all models, the effect of age of the calf was greater than the other effects tested. The use of actual weights in models without SB in GC allowed for better genetic connectivity between GC, and higher accuracy in the genetic evaluations. The estimates of (co)variance and genetic parameters were similar among models but the information criteria (BIC, CAIC) indicated that the most adequate model considered GC as a random effect, constituted by the effects of herd, year of birth, management group and the effect of trimester of birth with the effect of calf sex independent from GC. For each model animals were classified by their genetic value (VG), subdivided into categories (animals without progeny, bulls and cows). For both weights (actual and adjusted) VG were similar considering contemporary group as fixed or random, with sex included or not in its composition and with greater differences between models with actual and adjusted weights. Accuracy was similar among compared models within each category, bulls having more accurate VG predictions than cows. Spearman correlation coefficients for animal rank using direct and maternal VGs and simple (Pearson) correlations for accuracies among different models were all high and significant (P<0.001) with the greatest difference observed comparing models with actual and adjusted weights. For bulls, the classification of individuals with actual weights in models with random GC was more adequate. Removal of animals without adjusted weights or in contemporary groups with less than five individuals would lead to the elimination of animals which would contribute to the genetic gain in the population.